import numpy as np 
import pandas as pd # data processing, CSV file 
import matplotlib.pyplot as plt
import seaborn as sns
import os

df = pd.read_csv("segmented_customers.csv")
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	3
1	2	1	21	15	81	4
2	3	0	20	16	6	3
3	4	0	23	16	77	4
4	5	0	31	17	40	3

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 6 columns):
 #   Column                  Non-Null Count  Dtype
---  ------                  --------------  -----
 0   CustomerID              200 non-null    int64
 1   Gender                  200 non-null    int64
 2   Age                     200 non-null    int64
 3   Annual Income (k$)      200 non-null    int64
 4   Spending Score (1-100)  200 non-null    int64
 5   cluster                 200 non-null    int64
dtypes: int64(6)
memory usage: 9.5 KB

df.describe()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
count	200.000000	200.000000	200.000000	200.000000	200.000000	200.000000
mean	100.500000	0.440000	38.850000	60.560000	50.200000	1.760000
std	57.879185	0.497633	13.969007	26.264721	25.823522	1.191427
min	1.000000	0.000000	18.000000	15.000000	1.000000	0.000000
25%	50.750000	0.000000	28.750000	41.500000	34.750000	1.000000
50%	100.500000	0.000000	36.000000	61.500000	50.000000	2.000000
75%	150.250000	1.000000	49.000000	78.000000	73.000000	2.000000
max	200.000000	1.000000	70.000000	137.000000	99.000000	4.000000

df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	3
1	2	1	21	15	81	4
2	3	0	20	16	6	3
3	4	0	23	16	77	4
4	5	0	31	17	40	3

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x1bf06268e50>

df=df.drop(['CustomerID'],axis=1)
df.head()

	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	19	15	39	3
1	1	21	15	81	4
2	0	20	16	6	3
3	0	23	16	77	4
4	0	31	17	40	3

sns.heatmap(df.corr())

<AxesSubplot:>

plt.figure(figsize=(7,7))
size=df['Gender'].value_counts()
label=['Female','Male']
color=['purple','Blue']
explode=[0,0.1]
plt.pie(size,explode=explode,labels=label,colors=color,shadow=True)
plt.legend()
plt.show()

plt.figure(figsize=(10,5))
sns.countplot(df['Age'])
plt.xticks(rotation=90)

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]),
 [Text(0, 0, '18'),
  Text(1, 0, '19'),
  Text(2, 0, '20'),
  Text(3, 0, '21'),
  Text(4, 0, '22'),
  Text(5, 0, '23'),
  Text(6, 0, '24'),
  Text(7, 0, '25'),
  Text(8, 0, '26'),
  Text(9, 0, '27'),
  Text(10, 0, '28'),
  Text(11, 0, '29'),
  Text(12, 0, '30'),
  Text(13, 0, '31'),
  Text(14, 0, '32'),
  Text(15, 0, '33'),
  Text(16, 0, '34'),
  Text(17, 0, '35'),
  Text(18, 0, '36'),
  Text(19, 0, '37'),
  Text(20, 0, '38'),
  Text(21, 0, '39'),
  Text(22, 0, '40'),
  Text(23, 0, '41'),
  Text(24, 0, '42'),
  Text(25, 0, '43'),
  Text(26, 0, '44'),
  Text(27, 0, '45'),
  Text(28, 0, '46'),
  Text(29, 0, '47'),
  Text(30, 0, '48'),
  Text(31, 0, '49'),
  Text(32, 0, '50'),
  Text(33, 0, '51'),
  Text(34, 0, '52'),
  Text(35, 0, '53'),
  Text(36, 0, '54'),
  Text(37, 0, '55'),
  Text(38, 0, '56'),
  Text(39, 0, '57'),
  Text(40, 0, '58'),
  Text(41, 0, '59'),
  Text(42, 0, '60'),
  Text(43, 0, '63'),
  Text(44, 0, '64'),
  Text(45, 0, '65'),
  Text(46, 0, '66'),
  Text(47, 0, '67'),
  Text(48, 0, '68'),
  Text(49, 0, '69'),
  Text(50, 0, '70')])

sns.boxplot(df['Gender'],df['Annual Income (k$)'])

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Gender', ylabel='Annual Income (k$)'>

plt.figure(figsize=(15,5))
sns.countplot(df['Annual Income (k$)'])

c:\users\user\appdata\local\programs\python\python38\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Annual Income (k$)', ylabel='count'>

plt.bar(df['Annual Income (k$)'],df['Spending Score (1-100)'])
plt.title('Spending Score (1-100) over Annual Income (k$))',fontsize=20)
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')

Text(0, 0.5, 'Spending Score (1-100)')

DBSCAN CLUSTERING

df.head()

	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	19	15	39	3
1	1	21	15	81	4
2	0	20	16	6	3
3	0	23	16	77	4
4	0	31	17	40	3

x=df.iloc[:,[1,3]].values

x.shape

(200, 2)

from sklearn.cluster import DBSCAN
db=DBSCAN(eps=1,min_samples=2,metric='euclidean')

model=db.fit(x)
label=model.labels_
label

array([-1,  0,  1,  2, -1, -1, -1, -1, -1,  3, -1,  4,  5,  2, -1, -1,  6,
       -1, -1,  4,  6, -1, -1,  3, -1,  7, -1, -1, -1, -1, -1, -1, -1, -1,
       -1,  0,  8,  3,  9, -1, -1, -1, 10, -1, -1, -1, -1, -1, -1, 11, -1,
       12, -1, -1, 13, 14, -1, -1, 15, 16, 17, 18, -1, -1, -1, 19, -1, -1,
       19, -1, 17, 14, -1, -1, -1, 20, -1, -1, 21, 14, -1, -1, -1, -1, -1,
       -1, -1, -1, 12, 13, -1, -1, -1, -1, 11, 21, -1, 15, 14, -1, -1, -1,
       -1, 20, -1, -1, -1, 16, -1, -1, -1, 18, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, 22, -1, -1, -1, -1, -1, 23, -1, 23, -1, -1,  1, 24,
       -1,  3, -1, -1, -1, 25, -1, 26, -1, -1, 10,  3, 27, 28,  8, -1, -1,
       23, 29, -1, -1, -1, -1,  3, -1,  7,  1, 25, -1, -1, -1, -1,  9, -1,
       -1, -1, -1, -1, -1, -1,  5, -1, -1, 28, -1, 26, -1, 24, -1, -1, -1,
       -1,  8, -1, 27, -1, -1, 22, 29, -1, -1,  3, -1,  7], dtype=int64)

from sklearn import metrics

#identifying the points which makes up our core points
sample_cores=np.zeros_like(label,dtype=bool)

sample_cores[db.core_sample_indices_]=True

#Calculating the number of clusters

n_clusters=len(set(label))- (1 if -1 in label else 0)
print('No of clusters:',n_clusters)

No of clusters: 30

y_means = db.fit_predict(x)
plt.figure(figsize=(10,10))
plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 50, c = 'black')
plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 50, c = 'violet')
plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 50, c = 'cyan')
plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s = 50, c = 'red')
plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s = 50, c = 'orange')
plt.scatter(x[y_means == 5, 0], x[y_means == 5, 1], s = 50, c = 'blue')
plt.scatter(x[y_means == 6, 0], x[y_means == 6, 1], s = 50, c = 'green')
plt.scatter(x[y_means == 7, 0], x[y_means == 7, 1], s = 50, c = 'purple')
plt.scatter(x[y_means == 8, 0], x[y_means == 8, 1], s = 50, c = 'yellow')
plt.scatter(x[y_means == 9, 0], x[y_means == 9, 1], s = 50, c = '#88c999')
plt.scatter(x[y_means == 10, 0], x[y_means == 10, 1], s = 50, c = '#ffd24d')
plt.xlabel('Overall 1-100')
plt.ylabel('Wages 1-100')
plt.title('Clusters of data')
plt.show()

HIERARCHICAL CLUSTERING

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns

import plotly as py
import plotly.graph_objs as go

import warnings
warnings.filterwarnings('ignore')

from sklearn import preprocessing 
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering 

df = pd.read_csv('segmented_customers.csv')
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	3
1	2	1	21	15	81	4
2	3	0	20	16	6	3
3	4	0	23	16	77	4
4	5	0	31	17	40	3

df.isnull().sum()

CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
cluster                   0
dtype: int64

df.describe()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
count	200.000000	200.000000	200.000000	200.000000	200.000000	200.000000
mean	100.500000	0.440000	38.850000	60.560000	50.200000	1.760000
std	57.879185	0.497633	13.969007	26.264721	25.823522	1.191427
min	1.000000	0.000000	18.000000	15.000000	1.000000	0.000000
25%	50.750000	0.000000	28.750000	41.500000	34.750000	1.000000
50%	100.500000	0.000000	36.000000	61.500000	50.000000	2.000000
75%	150.250000	1.000000	49.000000	78.000000	73.000000	2.000000
max	200.000000	1.000000	70.000000	137.000000	99.000000	4.000000

plt.figure(1 , figsize = (15 , 6))
n = 0 
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1 , 3 , n)
    plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
    sns.distplot(df[x] , bins = 15)
    plt.title('Distplot of {}'.format(x))
plt.show()

label_encoder = preprocessing.LabelEncoder() 

df['Gender'] = label_encoder.fit_transform(df['Gender'])
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	3
1	2	1	21	15	81	4
2	3	0	20	16	6	3
3	4	0	23	16	77	4
4	5	0	31	17	40	3

plt.figure(1, figsize = (16 ,8))
sns.heatmap(df)
plt.show()

plt.figure(1, figsize = (16 ,8))
dendrogram = sch.dendrogram(sch.linkage(df, method  = "ward"))

plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()

hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage ='average')

y_hc = hc.fit_predict(df)
y_hc

array([3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,
       3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 2,
       3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 1, 2, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1], dtype=int64)

df['cluster'] = pd.DataFrame(y_hc)

trace1 = go.Scatter3d(
    x= df['Age'],
    y= df['Spending Score (1-100)'],
    z= df['Annual Income (k$)'],
    mode='markers',
     marker=dict(
        color = df['cluster'], 
        size= 10,
        line=dict(
            color= df['cluster'],
            width= 12
        ),
        opacity=0.8
     )
)
data = [trace1]
layout = go.Layout(
    title= 'Clusters using Agglomerative Clustering',
    scene = dict(
            xaxis = dict(title  = 'Age'),
            yaxis = dict(title  = 'Spending Score'),
            zaxis = dict(title  = 'Annual Income')
        )
)
fig = go.Figure(data=data, layout=layout)
py.offline.iplot(fig)

X = df.iloc[:, [3,4]].values
plt.scatter(X[y_hc==0, 0], X[y_hc==0, 1], s=100, c='red', label ='Cluster 1')
plt.scatter(X[y_hc==1, 0], X[y_hc==1, 1], s=100, c='blue', label ='Cluster 2')
plt.scatter(X[y_hc==2, 0], X[y_hc==2, 1], s=100, c='green', label ='Cluster 3')
plt.scatter(X[y_hc==3, 0], X[y_hc==3, 1], s=100, c='purple', label ='Cluster 4')
plt.scatter(X[y_hc==4, 0], X[y_hc==4, 1], s=100, c='orange', label ='Cluster 5')
plt.title('Clusters of Customers (Hierarchical Clustering Model)')
plt.xlabel('Annual Income(k$)')
plt.ylabel('Spending Score(1-100)')
plt.show()

K-MEANS CLUSTERING

from sklearn.cluster import KMeans
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from matplotlib import pyplot as plt
%matplotlib inline

df = pd.read_csv('segmented_customers.csv')
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	3
1	2	1	21	15	81	4
2	3	0	20	16	6	3
3	4	0	23	16	77	4
4	5	0	31	17	40	3

plt.scatter(df['Age'], df['Annual Income (k$)'])

<matplotlib.collections.PathCollection at 0x1bf0c31f460>

km = KMeans(n_clusters=3)
km

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',
       random_state=None, tol=0.0001, verbose=0)

y_predicted = km.fit_predict(df[['Age', 'Annual Income (k$)']])

y_predicted

array([2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 2,
       0, 2, 0, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2,
       0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2,
       0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1])

df['cluster'] = y_predicted
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	19	15	39	2
1	2	1	21	15	81	2
2	3	0	20	16	6	2
3	4	0	23	16	77	2
4	5	0	31	17	40	2

km.cluster_centers_

array([[55.03448276, 50.34482759],
       [34.34782609, 82.82608696],
       [28.36      , 31.44      ]])

df1 = df[df.cluster==0]
df2 = df[df.cluster==1]
df3 = df[df.cluster==2]

plt.scatter(df1.Age,df1['Annual Income (k$)'],color='green')
plt.scatter(df2.Age,df2['Annual Income (k$)'],color='red')
plt.scatter(df3.Age,df3['Annual Income (k$)'],color='black')

plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')
plt.xlabel('Age')
plt.ylabel('Annual Income (k$)')
plt.legend()

<matplotlib.legend.Legend at 0x1bf0c3a2370>

scaler = MinMaxScaler()

scaler.fit(df[['Annual Income (k$)']])
df['Annual Income (k$)'] = scaler.transform(df[['Annual Income (k$)']])

scaler.fit(df[['Age']])
df['Age'] = scaler.transform(df[['Age']])

df.head()
df

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	0.019231	0.000000	39	2
1	2	1	0.057692	0.000000	81	2
2	3	0	0.038462	0.008197	6	2
3	4	0	0.096154	0.008197	77	2
4	5	0	0.250000	0.016393	40	2
...	...	...	...	...	...	...
195	196	0	0.326923	0.860656	79	1
196	197	0	0.519231	0.909836	28	1
197	198	1	0.269231	0.909836	74	1
198	199	1	0.269231	1.000000	18	1
199	200	1	0.230769	1.000000	83	1

200 rows × 6 columns

km = KMeans(n_clusters=3)
y_predicted = km.fit_predict(df[['Age','Annual Income (k$)']])
y_predicted

array([0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0,
       2, 0, 2, 0, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 2, 0, 2, 0,
       2, 0, 2, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 0,
       2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 1, 0, 2, 2, 0, 2, 2, 0, 2, 2, 0,
       0, 2, 2, 0, 2, 1, 0, 0, 2, 0, 2, 0, 0, 2, 2, 0, 2, 0, 2, 2, 2, 2,
       2, 0, 1, 0, 0, 0, 2, 2, 2, 2, 0, 1, 1, 1, 0, 1, 1, 1, 2, 1, 2, 1,
       1, 1, 0, 1, 1, 1, 0, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,
       2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1])

df['cluster']=y_predicted
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)	cluster
0	1	1	0.019231	0.000000	39	0
1	2	1	0.057692	0.000000	81	0
2	3	0	0.038462	0.008197	6	0
3	4	0	0.096154	0.008197	77	0
4	5	0	0.250000	0.016393	40	0

km.cluster_centers_

array([[0.15264423, 0.21016906],
       [0.33653846, 0.58435792],
       [0.72175481, 0.29943648]])

df1 = df[df.cluster==0]
df2 = df[df.cluster==1]
df3 = df[df.cluster==2]
plt.scatter(df1.Age,df1['Annual Income (k$)'],color='green')
plt.scatter(df2.Age,df2['Annual Income (k$)'],color='red')
plt.scatter(df3.Age,df3['Annual Income (k$)'],color='black')
plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')
plt.legend()

<matplotlib.legend.Legend at 0x1bf0c4261c0>

sse = []
k_rng = range(1,10)
for k in k_rng:
    km = KMeans(n_clusters=k)
    km.fit(df[['Age','Annual Income (k$)']])
    sse.append(km.inertia_)

plt.xlabel('K')
plt.ylabel('Sum of squared error')
plt.plot(k_rng,sse)

[<matplotlib.lines.Line2D at 0x1bf0c732b20>]